| Autor: |
Karpov OE; National Medical and Surgical Center named after N. I. Pirogov, Ministry of Healthcare of the Russian Federation, 105203 Moscow, Russia., Grubov VV; Research and Production Company 'Immersmed', Moscow 105203, Russia.; Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Kazan, Russia., Maksimenko VA; Research and Production Company 'Immersmed', Moscow 105203, Russia.; Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Kazan, Russia., Utaschev N; National Medical and Surgical Center named after N. I. Pirogov, Ministry of Healthcare of the Russian Federation, 105203 Moscow, Russia., Semerikov VE; Research and Production Company 'Immersmed', Moscow 105203, Russia., Andrikov DA; Research and Production Company 'Immersmed', Moscow 105203, Russia., Hramov AE; Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Kazan, Russia. |
| Abstrakt: |
Extreme events are rare and sudden abnormal deviations of the system's behavior from a typical state. Statistical analysis reveals that if the time series contains extreme events, its distribution has a heavy tail. In dynamical systems, extreme events often occur due to developing instability preceded by noise amplification. Here, we apply this theory to analyze generalized epileptic seizures in the human brain. First, we demonstrate that the time series of electroencephalogram (EEG) spectral power in a frequency band of 1-5 Hz obeys a heavy-tailed distribution, confirming the presence of extreme events. Second, we report that noise on EEG signals gradually increases before the seizure onset. Thus, we hypothesize that generalized epileptic seizures in humans are the extreme events emerging from instability accompanied by preictal noise amplification similar to other dynamical systems. |
